Data Visualizations
October 30, 2025
How do we make this plot?
Two Questions:
What do we want R to do? (What is the goal?)
What does R need to know?
How do we make this plot?
Goal: scatterplot = a plot with points
What does R need to know?
data source:
Births2015aesthetics:
date -> xbirths -> y
points
How do we make this plot?
Layers: layer 0
Layers: layer 1
Layers: layer 2
Layers: layer 3
How do we make this plot?
What has changed?
- new aesthetic: mapping color to day of week
How do we make this plot?
How do we make this plot?
How do we make this plot?
How do we make this plot?
How do we make this plot?
Now there are two layers: one with points and one with lines
The layers are placed one on top of the other: the points are below and the lines are above.
dataandaesspecified inggplot()affect all geoms
What does this code do?
This is mapping the color aesthetic to a new variable with only one value (“navy”).
So all the dots get set to the same color, but it’s not navy.
Setting vs. Mapping
If we want to set the color to be navy for all of the dots, we do it outside the aes() designation:
- Note that
color = "navy"is now outside of the aesthetics list. That’s howggplot2distinguishes between mapping and setting.
How do we make this plot?
How do we make this plot?
ggplot()establishes the default data and aesthetics for the geoms, but each geom may change these defaults.good practice: put into
ggplot()the things that affect all (or most) of the layers; rest ingeom_XXXX()
geom_*() pics
Let’s try geom_area
Let’s try geom_area
Who are the people in the study?
Who are the people in the study?
Who are the people in the study?
Who are the people in the study?
How old are people in the HELP study?
`stat_bin()` using `bins = 30`. Pick better value `binwidth`.
Notice the messages
stat_bin: Histograms are not mapping the raw data but binned data.
stat_bin()performs the data transformation.binwidth: a default binwidth has been selected, but we should really choose our own.
Setting the binwidth manually
How old are people in the HELP study? – Other geoms
Selecting stat and geom manually
Every geom comes with a default stat
- for simple cases, the stat is
stat_identity()which does nothing - we can mix and match geoms and stats however we like
Selecting stat and geom manually
Every stat comes with a default geom, every geom with a default stat
- we can specify stats instead of geom, if we prefer
- we can mix and match geoms and stats however we like
More combinations
More combinations
More combinations
How much drinking? (i1)
How much drinking? (i1)
How much drinking? (i1)
Covariates: Adding in more variables
Using color and linetype:
Using color and facets
Boxplots
Boxplots use stat_quantile() (five number summary).
The quantitative variable must be y, and there must be an additional x variable.
Horizontal boxplots
Horizontal boxplots are obtained by flipping the coordinate system:
coord_flip()may be used with other plots as well to reverse the roles ofxandyon the plot.
Axes scaling with boxplots
We can scale the continuous axis
Give me some space
We’ve triggered a new feature: dodge (for dodging things left/right). We can control how much if we set the dodge manually.
Issues with bigger data
- Although we can see a generally positive association (as we would expect), the overplotting may be hiding information.
[1] 10000 76
Using alpha (opacity)
One way to deal with overplotting is to set the opacity low.
geom_density2d
Alternatively (or simultaneously) we might prefer a different geom altogether.
Multiple layers
Multiple layers
themes
jitterdodge()
A little bit of everything
ggplot(data = HELP_data, aes(x = substance, y = age, color = children)) +
geom_boxplot(coef = 10, position = position_dodge(width=1)) +
geom_point(aes(fill = children), alpha=.5,
position = position_jitterdodge(dodge.width=1, jitter.width = 0.2)) +
facet_wrap(~homeless) +
labs(title = "HELP clinical trial at detoxification unit")